The present invention relates to a process for the automatic recognition or identification of objects liable to overlap. It applies to vision machines and more particularly to robotics, in which studies are presently directed at so-called "intelligent" robots able to interpret data from the external medium. The recognition or identification of partly hidden objects, e.g. as a result of a partial overlap of said objects, is of considerable importance for the next generation of vision machine.
Different processes for the recognition of objects liable to overlap are known in the art. One of these processes consists of segmenting the contour line of the image of each object to be recognized and defining in the segmentation of each contour "privileged" segments, which are in fact the longest segments. This definition of privileged segments is carried out during a learning period.
In this known process, during the period of recognizing the different objects, hypotheses are issued on the basis of an examination of the privileged segments and associated segments recognized in the contour of the image of the objects to be recognized. A "label" is then allocated to each privileged segment recognized in the contour of the image of the objects to be recognized. Then, on the basis of the different labels obtained in this way, an overall criterion is defined, which is a measure of the consistency and non-ambiguity of the labelling system. A quality score is then calculated for evaluating the hypotheses.
This type of process takes a long time and is very expensive, because it generates a large quantity of hypotheses, which must be checked according to complex criteria for measuring the consistency and non-ambiguity of the labels. This type of process is e.g. described in the doctorate thesis entitled "A bidimensional vision system in industrial robotics" by N. AYACHE, 1983.
The invention aims at obviating the disadvantages of this type of process and more particularly at permitting an automatic, fast recognition of objects liable to overlap, without it being necessary to emit, during this recognition, a large number of hypotheses and criteria for checking said hypotheses. These objectives are more particularly attained by using a particular segmentation of the contours of the images of the reference objects and the images of the object to be recognized and as a result of the choices of the pairs of characteristic segments making it possible to define "transition vectors" on the basis of which the hypotheses and recognition criteria are developed.